Adaptive Receiver Learning for Real-World Wireless Signal Reception
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Solution Overview
Problem
Existing wireless communication systems struggle to fully utilize the advantages of machine learning due to the lack of sufficient learning data from actual environments, leading to suboptimal performance in receiver models.
Innovation Solution
A method is proposed to enhance wireless communication reception by using machine learning-based receiver models trained in actual environments, incorporating both channel and hardware data for supervised learning, allowing for optimized performance across various use environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning-based receiver models are trained only with simulation or representative environment data, then the system complexity is reduced and ease of manufacture is improved, but the receiver performance and reliability in actual environments deteriorate due to lack of real environment data
Solution Approach 1:
The patent applies preliminary action by first training a basic receiver model using simulation data or representative environment data before deployment. This preliminary training provides a foundational model that can then be adapted to actual environments through additional learning with real environment data, thereby resolving the contradiction between ease of initial training and reliability in actual deployment
Solution Approach 2:
The patent implements dynamics by enabling the receiver model to adapt dynamically from a static simulation-trained model to a dynamic model that learns from actual environment data. The system transitions from a fixed basic receiver model to an adaptable model that can be retrained or fine-tuned based on real-world performance feedback, improving reliability while maintaining manageable complexity
2Reliability
If additional learning is performed using actual environment data, then the receiver performance and reliability are improved, but the device complexity and training time increase
Solution Approach 1:
The patent applies segmentation by dividing the learning process into distinct stages: first training a basic receiver model with simulation or representative data, then performing separate additional learning with actual environment data. This segmented approach allows each training phase to focus on specific aspects, reducing overall complexity while achieving high reliability through cumulative learning
Solution Approach 2:
By performing preliminary training with simulation data before actual environment learning, the patent reduces the complexity of the additional learning phase. The basic model already captures fundamental patterns, so the subsequent actual environment training only needs to fine-tune and adapt to specific real-world conditions, rather than learning from scratch
3Ease of operation
If a single basic receiver model is used for all environments, then the ease of operation is improved and device complexity is reduced, but the adaptability to different use environments deteriorates
Solution Approach 1:
The patent implements universality by designing a basic receiver model that serves as a universal foundation applicable to all environments. This basic model can then be adapted to specific environments through additional learning, allowing the same model architecture to function universally across diverse conditions while maintaining ease of operation through a standardized base
4Productivity
If receiver models are optimized for specific environments through additional learning, then the reception quality and productivity are improved, but the loss of time for data collection and retraining increases
Solution Approach 1:
The patent reduces time loss by performing preliminary training with simulation or representative data before deployment. This advance preparation creates a functional basic model that requires minimal additional learning when deployed in actual environments, significantly reducing the time needed for post-deployment optimization while maintaining high reception quality
Data Source
AI summary
The present disclosure proposes a method and procedure for performing additional learning using data secured in an actual environment in a basic receiver model optimized for a representative environment and a method of operating a plurality of customized receiver models secured through this and the basic receiver model together.